Course DescriptionTraditionally, researchers in the field of computer vision have been hand-crafting appropriate physical/statistical models of objects/natural scenes for building computer vision systems. Recent advances in imaging and computing technology make it possible to capture and process large amounts of visual data efficiently. This lead to increasing use of machine learning techniques for model learning in computer vision. A model learned from large visual datasets is less likely to be brittle than a model hand-crafted by a designer. In this course, we will explore recent successful computer vision methods based on machine learning. The course will be organized as a combination of lectures by the instructor and paper presentation by the students. After we study a new machine learning technique, we will read and discuss a paper that makes use of that technique. Each student will do a paper presentation, as well as a programming project.
Machine Learning topics will be selected from the following:
PrerequisitesA course on computer vision or image processing; strong programming skills in C or C++; familiarity with statistics, calculus, linear algebra. Students lacking these requirements should speak with the instructor for obtaining permission to enroll.
TextbookThere will be no required textbook in this course. The course will be based on papers that I will hand out for reading. For reference, students can use the following books:
Student EvaluationGrades will be based on:
Final ProjectA student will chose a final project in consultation with me. The project must be related to computer vision and must make use machine learning techniques. Students should select their final project topic by November 2, and the final project written report and the code is due on January 20.
Ethical ConductPlagiarism:Students must write their essays and assignments in their own words. Whenever students take an idea, or a passage from another author, they must acknowledge their debt both by using quotation marks where appropriate and by proper referencing such as footnotes or citations. Plagiarism is a major academic offence (see Scholastic Offence Policy in the Western Academic Calendar).